library(plotly)
library(dplyr)
library(lubridate)

This is an exploratory data notebook. It’s a sort of sandbox to prepare and do some quick visualizations prior to building the Shiny App.

Load data

Some data was already prepped for this, so we’ll start by loading the file and doing some preliminary plotting.

indices_table <- read.csv("data/prepped/keywest-withfish.csv")

indices_table <- indices_table %>%
  mutate(datetime = ymd_hms(datetime))

Quick line plot

fig <- plot_ly(data = indices_table, height = 400)

# Adding the line trace
fig <- fig %>%
  add_trace(
    x = ~datetime,
    y = ~ACI,
    type = 'scatter',
    mode = 'lines',
    name = 'ACI'
  )

# Adding the marker trace
fig <- fig %>%
  add_trace(
    x = ~datetime,
    y = ~ACI,
    type = 'scatter',
    mode = 'markers',
    name = 'Presence',
    marker = list(size = ~Em * 7)
  )

# Updating layout
fig <- fig %>%
  layout(
    legend = list(itemclick = FALSE, 
                  itemdoubleclick = "toggleothers" 
                  #itemsizing = 'constant'
                  )
    
  )

# Show the plot
fig
NA

Correlation matrix

# Select only the acoustic index columns
index_columns <- names(indices_table)[3:15]

# Calculate the correlation matrix
cor_matrix <- cor(indices_table[index_columns])

# Plot the correlation matrix
corrplot(cor_matrix, type = 'lower', order = 'hclust', tl.col = 'black',
        cl.ratio = 0.2, tl.srt = 45, col = COL2('PuOr', 10))

# corrplot(cor_matrix)
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